Optimized New Efficient Load Balancing Technique For Scheduling Virtual Machine

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1 Optimized New Efficient Load Balancing Technique For Scheduling Virtual Machine B.Preethi 1, Prof. C. Kamalanathan 2, 1 PG Scholar, 2 Professor 1,2 Bannari Amman Institute of Technology Sathyamangalam, Pin no bpreethiece@gmail.com, 2 kamalanadhan@gmail.com Abstract Cloud computing offers advantage in cost, scalability, flexibility to its users. Load balancing is essential for optimization of resources in distributed environments. The major goal of the cloud computing service providers is to use cloud computing resources efficiently to enhance the overall performance in all aspects. Cloud load balancing simply distributes workload across multiple servers or data centers, network links, central processing units, disk drives, on the cloud, i.e. within the Internet itself. This method, as other load balancing methods, helps to achieve optimal resource utilization, maximize throughput, minimize response time, and avoid overload of resources. The performance of the algorithm is analyzed using Cloudsim simulator and cloud analyst.the simulation result ensures that all the processors in the system as well as in the network does approximately equal amount of work at any instant of time by comparing with other algorithms by its spoofing abilities and paving way for green computing. Keywords: Virtual Machine, Load balancing, Cloudsim, Cloud analyst, Cloud Computing, green computing 1. INTRODUCTION Cloud computing is a comprehensive solution that delivers IT as a service. It is an Internetbased computing solution where shared resources are provided like electricity distributed on the electrical grid. Computers in the cloud are configured to work together and the various applications use the collective computing power as if they are running on a single system. Cloud Computing is a new technology used by industry and in many areas for storing and retrieving the files and necessary documents where distributed computing forms the basis. The main issues in Cloud Computing lies in scheduling the incoming request in an efficient way with minimum response time and also the resources should not be underutilized. Algorithms like Round Robin, FCFS and Throttled are good in serving the client request with minimum response time. But the problem of high communication delays and underutilization of resources are not fulfilled in executing the client request which leads to imbalance of cloud system The flexibility of cloud computing is a function of the allocation of resources on demand. This facilitates the use of the system's cumulative resources, negating the need to assign specific hardware to a task. Before cloud computing, websites and server-based applications were executed on a specific system. With the advent of cloud computing, resources are used as an aggregated virtual computer. This amalgamated configuration provides an environment where applications execute independently without regard for any particular configuration. 2. NEED OF CLOUD COMPUTING There are valid and significant business and IT reasons for the cloud computing paradigm shift. The fundamentals of outsourcing as a solution apply.

2 Reduced cost: Cloud computing can reduce both capital expense (CapEx) and operating expense (OpEx) costs because resources are only acquired when needed and are only paid for when used. Refined usage of personnel: Using cloud computing frees valuable personnel allowing them to focus on delivering value rather than maintaining hardware and software. Robust scalability: Cloud computing allows for immediate scaling, either up or down, at any time without long-term commitment. Figure 1. Layers in cloud The cloud computing model is comprised of a front end and a back end. These two elements are connected through a network, in most cases the Internet. The front end is the vehicle by which the user interacts with the system; the back end is the cloud itself. The front end is composed of a client computer, or the computer network of an enterprise, and the applications used to access the cloud. The back end provides the applications, computers, servers, and data storage that creates the cloud of services. The cloud concept is built on layers, each providing a distinct level of functionality. This stratification of the cloud's components has provided a means for the layers of cloud computing to becoming a commodity just like electricity, telephone service, or natural gas. The commodity that cloud computing sells is computing power at a lower cost and expense to the user. Cloud computing is poised to become the next mega-utility service. In this section load balancing algorithms used in cloud computing environment are briefly summarized. The Active load balancing algorithm maintains all the current information about the virtual machines and number of requests currently allocated. Initially when a request arrives, it selects the VM based on which machine is least loaded and returns id of the VM to the data center controller. When there are more than one VM is found, the first identified is returned. Data center controller notifies the Active VM load balancer of the new allocation. The allocation using Throttled algorithm is completely based on the virtual machine. The client first sends requests to the load balancer to check for the availability of VM which is capable of handling and processing the request. The Active Monitoring Load Balancer maintains information about each VM s and the number of request currently allocated to which VM when a request is allocate a new VM arrives. If there are more than one VM, the first identified is selected AMLB returns the VM id to the data centres controller. The data centres controller send the request to the VM identified by that id. The data centre controller notifies the AMLB to new allocation and cloudlets is sent to it. Data centers consist of both physical and virtualized IT resources. The physical IT resource layer refers to the facility infrastructure that houses computing/networking systems and equipment, together with hardware systems and their operating systems (Figure 1). The resource abstraction and control of the virtualization layer is comprised of operational and management tools that are often based on virtualization platforms that abstract the physical computing and networking IT resources as virtualized components that are easier to allocate, operate, release, monitor, and control 3. LOAD BALANCING

3 actual implementation and needs not care about the actual implementation. The load balancer makes one, two, or two-hundred resources - whether physical or virtual - appear to be one resource; this decouples the user from the physical implementation of the application and allows the internal implementation to grow, to shrink, and to change without any obvious affect on the user. Figure 2. Virtualization Neural network is classified into two types: Feed-forward Network: In feed-forward type of network signal travels only in one direction.i.e. from input to output. There is no feedback.i.e. output layer don t provide input to input layer. Feed-backward Network: In feed-backward type of network signal can travel in both directions i.e. forward and backward. 4. NEED OF LOAD BALANCING With A load balancer provides the means by which instances of applications can be provisioned and deprovisioned automatically, without requiring changes to the network or its configuration. It automatically handles the increases and decreases in capacity and adapts its distribution decisions based on the capacity available at the time a request is made. Because the end-user is always directed to a virtual server, or IP address, on the load balancer the increase or decrease of capacity provided by the provisioning and de-provisioning of application instances is non-disruptive. As is required by even the most basic of cloud computing definitions, the end user is abstracted by the load balancer from the Figure 3. Load balancing 4.1 DYNAMIC LOAD BALANCING Dynamic load balancing is a major key for a successful implementation of cloud environments. The main goal of a cloud-based architecture is to provide elasticity, the ability to expand and contract capacity on-demand. Sometimes additional instances of an application will be required in order for the architecture to scale and meet demand. That means there is a need for a mechanism to balance requests between two or more instances of that application. The mechanism most likely to be successful in performing such a task is a load balancer. There s no other way to assume increased load other than adding new instances and distributing that load with software or hardware Similarly, when the additional instances of that application are de-provisioned, the changes to the network configuration need to be reversed, but software and hardware load balance is easy to scale up or scale down. Obviously a manual process would be time consuming and inefficient, effectively erasing the benefits gained by introducing a cloud-based architecture in the first place. The below is an example of how dynamic load balancing can be

4 implemented. Let s assume that cloud management console, or a custom developed application or cloud tool kit, triggers an event that indicates a new instance is required to maintain availability. How it determines capacity limitations may be based on VM status via VMware APIs or data received from the load balancer, or a combination both. the basis of this assumption they had analysis that which factor greatly degrades the performance of the system which are added by developer and are not specified in the requirements analysis. In our work we are considering the artificial neuron system. When we add some extra features to this system the performance of this system degrades. In our work we will define certain set of rules which will check that which extra features degrades the performance of the system and also check the impact of these features. There are many server nodes comprising many clusters. Each server node and clusters are attached with counter for calculating the processed requests. Request is allocated based on value of the counter variable. If a suppose server node A is having 45 as counter value and server node B having 56 as value,then the incoming request is allocated to A as it is processing less job at that time. Each time when a request is allocated to a particular cluster in a node,its counter value is increased to one. After that respected server node is updated with that value. ALGORITHM Least VM assign algorithm() Inputs: Job= X1,X2, Xn Initialize all the available VM to zero Old VM OVM1,OVM2,..,OVMn=0; New VM NVM1,NVM2,,NVMN=0; If (OVM1==1) NVM1+=1; Else if NVM1+=1; SelectedVM=LeastVMof(NVM1,NVM2,,NVMN) If(Selected VM is free) Selected VM=req(X1,X2, Xn) The work flow is described as in the following section. The main idea behind the proposed algorithm is to reduce the ideal state of the server. This is done by calculating the task starting and end timing and allocates the available resource and calculates the timing of task at current time and de allocates the resource (task request) according to the calculated timing. The following diagram shows the flow chart of the proposed algorithm.. SIMULATION RESULTS Figure 4. Equal utilization of resources Figure 5. shows the screen shot of output window of cloud analyst simulator 5. CONCLUSIONS AND FUTURE WORK

5 Choosing right load balancer at the beginning is imperative to the success of complex implementations later. So far we studied about the various load balancing algorithms. The proposed algorithm least VM assign method distribute workload across multiple computers to achieve optimal resource utilization with minimum response time. In the proposed work, an efficient algorithm is designed which manages the load at the server by considering the current status of the all available VMs and calculating the task arriving time. The VM-assign load balancer mainly focuses on the efficient utilization of the resources /VM. It is proved that our proposed algorithm optimally distributes the load and hence under / over utilization (VMs) situations will not arise. In future more clients can be added dynamically. References [1] Al Nuaimi, Nadar Mohamed, Marium Al Nuaimi and Jameela Al Jaroodi, A Survey of Load Balancing in Cloud Computing: Challenges and Algorithms, 2012 IEEE Second symposium on Network Computing and Applications. [2] Huankai Chen, Professor Frank Wang, Dr Na Helian and Gbola Akanmu, User-Priority Guided Min-Min Scheduling Algorithm For Load Balancing in Cloud Computing. [3] Andrzej Goscinski, Michael Brock, Future Generation Computer Systems Towards Dynamic and [4] Attribute based Publication, Discovery and Selection for Cloud Computing, 2010 Elsevier. [5] M. Dorigo, V. Maniezzo and A. Colorni, Ant System: Optimization by a Colony of Cooperating Agents, IEEE Transactions on Systems, Man, and Cybernetics, PP , [6] C.W. Chiang, Y.C. Lee, C.N. Lee and T.Y. Chou, Ant Colony Optimization for Task Matching and Scheduling, IEEE Proceedings on Computers and Digital Techniques, 153 (6), pp , [7] S.Hemachander Harikrishna, R.Backiyalakshmi, A Game Theory Modal Based On Cloud Computing For Public Cloud, IOSR Journal of Computer Engineering, Volume 16, Issue 2, Ver. XII (Mar-Apr. 2014). [8] R. W. Lucky, Cloud computing, IEEE Journal of Spectrum, Vol. 46, No. 5, May 2009, pages [9] M. D. Dikaiakos, G. Pallis, D. Katsa, P. Mehra, and A. Vakali, Cloud Computing: Distributed Internet Computing for IT and Scientific Research, IEEE Journal of Internet Computing, Vol. 13, No. 5, September/October 2009, pages